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| Hauptseite > Publikationsdatenbank > CytoNet: A Foundation Model for the Human Cerebral Cortex > print |
| Hauptseite > Publikationsdatenbank > CytoNet: A Foundation Model for the Human Cerebral Cortex > print |
| 001 | 1048160 | ||
| 005 | 20251117202148.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2511.01870 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-04528 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-04528 |
| 100 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a CytoNet: A Foundation Model for the Human Cerebral Cortex |
| 260 | _ | _ | |c 2025 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1763385212_4679 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a To study how the human brain works, we need to explore the organization of the cerebral cortex and its detailed cellular architecture. We introduce CytoNet, a foundation model that encodes high-resolution microscopic image patches of the cerebral cortex into highly expressive feature representations, enabling comprehensive brain analyses. CytoNet employs self-supervised learning using spatial proximity as a powerful training signal, without requiring manual labelling. The resulting features are anatomically sound and biologically relevant. They encode general aspects of cortical architecture and unique brain-specific traits. We demonstrate top-tier performance in tasks such as cortical area classification, cortical layer segmentation, cell morphology estimation, and unsupervised brain region mapping. As a foundation model, CytoNet offers a consistent framework for studying cortical microarchitecture, supporting analyses of its relationship with other structural and functional brain features, and paving the way for diverse neuroscientific investigations. |
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| 773 | _ | _ | |a 10.48550/ARXIV.2511.01870 |
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